An-Hour-Ahead Solar Power Forecasting Using Artificial Neural Networks in Taiwan
碩士 === 國立臺灣大學 === 電機工程學研究所 === 107 === The development plan of the PV power plant in Taiwan with high penetration makes the grid operator has to prepare the strategies to mitigate its intermittency behaviour. The power grid must be more flexible to receive the fluctuated power from PV. One of the ec...
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| Format: | Others |
| Language: | en_US |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/dtdmp3 |
| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 107 === The development plan of the PV power plant in Taiwan with high penetration makes the grid operator has to prepare the strategies to mitigate its intermittency behaviour. The power grid must be more flexible to receive the fluctuated power from PV. One of the economical ways is to conduct a solar power forecasting.
The solar power forecasting is made using artificial neural networks in this thesis. The networks are trained using backpropagation and extreme learning machine. The extreme learning machine was used due to its advantages in accuracy and computational time over backpropagation neural networks. The persistence model is used as the reference for the performance index. It can also be the input of the combined forecasting model to improve accuracy.
Later on, the month-based segmentation forecasting was investigated to accommodate the seasonal variation of generated PV power output. At the end of the research, it is found out that the segmented solar power forecasting has a better performance than the forecasting with only one model for the whole year.
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